Spatial Demography最新文献

Economic complexity (EC) measures the diversification and domestic comparative advantage of industry in an economy. Originally applied to studies of global economic development, dominant frameworks suggest the extent of a place's economic capabilities underlies its economic and population growth potential and ascribe to the universalistic notion that economic growth generates population growth. Limited research has extended the observed linkages between EC and economic and population growth in a subnational context, focusing solely on metropolitan/micropolitan contexts and neglecting potential spatialized forces that might promote variation in the relationship, especially in rural areas. After computing EC estimates for Commuting Zones (CZs), a typology inclusive of metropolitan, micropolitan, and non-metropolitan areas, we use spatial modeling techniques to investigate whether economic complexity relates to population growth and net in-migration uniformly by rurality and for ethnoracial groups. In contrast to universalistic assumptions and our expectations that complexity would be less predictive of growth in non-metro contexts, we find that higher EC more strongly associates with population growth and net in-migration in non-metro contexts. Our findings also suggest that there is a racial dimension to EC, with higher EC CZs experiencing net-inflows of White populations and net-outflows of Asian, Black, Hispanic, and Indigenous populations. Study results suggest spatially and racially disparate implications of regional economic growth and development, challenging conventional assumptions of the widely distributed benefits of economic development on less resourced ethnoracial groups, especially in non-metro contexts.

The population health literature recognizes structural racism as a fundamental determinant of racialized health disparities. However, the role of structural racism in the continued persistence of racialized disparities in sexually transmitted infections (STIs) has not been investigated despite Black Americans' disproportionate experience of STIs in comparison to White Americans. Past research has largely investigated individual racial/ethnic identity as an individual-level factor predictive of STIs, failing to engage with the multitude of racially structured contexts which likely shape STI rates. This study combines multiple datasets, including data from the Centers for Disease Control and Prevention, the American Community Survey, and the Current Population Survey, to conduct a state-level analysis investigating the role of structural racism in contributing to Black-White racialized disparities in STIs between 2010 and 2020. Random effects spatial autoregressive models suggest that structural racism contributes to Black-White racialized disparities in STIs. This research contributes to literatures on structural racism and population health by better understanding how racialized state-level institutions shape the contraction of infections. The results have important implications for understanding states as institutional actors relevant for patterns of population health and the geography of racism.

Gun violence takes an enormous toll on neighborhoods and their residents in important ways. While research has identified that violence makes neighborhoods less appealing and livable, few studies have fully quantified the effect of violence on neighborhoods' vitality and dynamism. In this study, we introduce the notion of 'neighborhood activity,' which we measure by the unique number of everyday visitors those neighborhoods receive from residents of other neighborhoods. Drawing on a large geographically-coded dataset of 30,000 gun violence incidents across US neighborhoods in conjunction with daily mobility pattern data based on 45 million mobile devices, we apply a quasi-experimental method to estimate the impact of gun violence on the number of visitors neighborhoods receive. We find that gun violence reduces neighborhoods' visibility significantly, but its consequences are disproportionately distributed among non-White neighborhoods that are far less popular to begin with. Our estimation results indicate that gun violence cost neighborhoods approximately 9 million visitors in the year 2019 alone.

Supplementary information: The online version contains supplementary material available at 10.1007/s40980-025-00139-1.

Research investigating association between patriarchy and demographic behavior is limited in India. The only study on this subject utilized 1981 Indian Census data to examine associations between patriarchy and fertility. We examined the association of patriarchy, measured using India Patriarchy Index (IPI), with total fertility rate (TFR) and excess female child mortality in India. Additionally, we examined independent associations of the 5 dimensions included in the IPI with the two outcomes. We used univariate and bivariate Local Indicators of Spatial Autocorrelation, multivariable ordinary least squares and spatial error- regressions to examine the associations. Spatial heterogeneity beyond the north-south divide was evident in the spatial association of IPI with TFR and excess female child mortality. Results show positive association of IPI with TFR and excess female child mortality. While son preference and socio-economic domination were positively associated with TFR, domination of men over women and son preference were positively associated with excess female child mortality. This study is the first of its kind to examine the association of a novel measure of patriarchy with TFR and excess female child mortality. As patriarchy is deep-rooted in Indian society, a great deal of effort is needed to shift these traditionally held social norms and practices.

The mental and physical toll of conflict-related stressors-such as safety concerns, food insecurity, and loss of income-may have significant and deleterious impacts on birth outcomes among pregnant women. Armed conflict disrupts people's lives, often limiting access to essential resources and healthcare services (e.g., abortion-related healthcare). We investigated the relationship between armed conflict and pregnancy termination among pregnant women (aged 15-49) in Burkina Faso using health data from the Performance Monitoring for Action (PMA) project and armed conflict event data from the Uppsala Conflict Data Project (UCDP). We found that pregnancies exposed to armed conflict in the first trimester were less likely to end in pregnancy termination compared to pregnancies that were not exposed to armed conflict in the first trimester, though we are unable to disentangle spontaneous versus induced terminations in these data. Thus, we cannot determine whether this finding is driven largely by one type of abortion or another, nor can we be sure that the effects are not counteracting each other. Because interruption of healthcare access is a common consequence of armed conflict, abortion-related services should be targeted and adapted to suit the specific needs and obstacles faced by pregnant women in conflict-affected places.

A geospatial model was developed to statistically assess the socioeconomic effects of coal mining in the greater Pittsburgh, Pennsylvania area by integrating home sale data, abandoned mine lands (AML) inventory “problem area” sites, and census demographic information. Results indicated that homes located within problem areas sold for an average of 28% ($58,600) less than homes outside of these regions. Demographic data revealed a notable disparity in the population distribution within Allegheny County mining problem areas as having a statistically significant larger Black population. This same trend was even more pronounced in urban areas. The study also established that areas influenced by past mining activities had a higher proportion of individuals without formal postsecondary education. Logistic regression models were created to analytically evaluate the relationship between predictor variables, specifically home sale price and Community Needs Index, to the probability of being situated within mining problem areas. The home sale analysis indicated a negative correlation between sale prices and the likelihood of residing in a mining-affected zone, implying that properties with lower prices are more commonly situated in these impacted areas. The CNI logistic regression model revealed a correlation between the probability of living in a mining problem area and overall higher community needs.

One potential explanation for the continuance of spousal violence in India is spousal violence spatial spillover effects, in which the occurrence of spousal violence in one family or area increases the incidence of spousal violence in a neighboring one. This study used the 2019–2021 Indian Demographic and Health Survey data to examine the hypothesis of a spillover effect of spousal violence in India and to ascertain a thorough analysis of the geographic relationship between hypothetical origin place of spousal violence and its neighbors, and the occurrence of spousal violence. Our multivariate spatial auto regressive model further suggests that in India there has spatial spillover effect of neighborhood characteristics on spousal violence. The findings imply that concentrating legislative initiatives to diminish spousal violence in one area may decrease the incidence in nearby areas, resulting in an overall reduction across the country. The current study argues that a shift away from country-level policies to localized strategies targeting on specific geographic clusters may be a more cost-effective but may be considered an efficient means for reducing spousal violence against women in India.

Adverse sexual and reproductive health outcomes resulting from risky sexual behaviours (RSB) among young adults disproportionately affect them and contribute to an increased burden of sexually transmitted diseases. Existing research in sub-Saharan Africa, has yielded contrasting findings on the factors associated with the sexual behaviour of young people. This paper presents a comprehensive examination of the geographical distribution and socio-demographic determinants of RSB among young people in Nigeria, focusing on three primary outcomes: unprotected sex, multiple sexual partners, and early sexual debut. This study used cross-sectional data from the 2018 Nigeria Demographic and Health Survey on young adults aged 15–24 years. Descriptive and inferential statistics, including Bayesian multivariate shared component spatial modelling were used to assess geographical and socio-demographic factors influencing RSB. Males in the North-West region exhibited a high prevalence of unprotected sex and early sexual debut while unprotected sex was more prevalent among females in the South–South region. Furthermore, individuals in the South-South and Central regions consistently showed a higher likelihood of engaging in all three indicators of RSB. Individuals who were employed as well as rural residents had an increased risk of early sexual debut and multiple sexual partners. Furthermore, mobile phone and internet access were found to impact RSB. These findings offer insights into the geographic distribution and socio-demographic determinants of RSB among young people, highlighting the need for tailored interventions. These insights can inform the development of targeted interventions, education programs, and policies to reduce the prevalence of RSB.

The implementation of place-based policies entails the construction of intervention areas (spatially contiguous areas in which the policies are adopted). Many approaches can be adopted for the definition of such areas. This paper reflects on the use of geographically weighted regression (GWR) models as a tool capable of supporting the definition process. The case study concerns Molise, a region in Southern Italy particularly affected by persistent and deep-rooted processes of depopulation. The dependent variable is the average annual rate of population change of municipalities of Molise across the 2011–2019. The independent variables are related to socio-economic profiles of each municipality. The results, contextualised using a broad overview of the Italian case, show that a key variable in the demographic dynamics of the municipalities of Molise is the labour market activity rate of women and that this variable drives a spatial instability that cannot be detected using global approaches and models. This proves the urgent need to expand the use of local thinking for the benefit of both applied demography and society.